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A new space--time multivariate approach for environmental data analysis

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  • Sandra De Iaco

Abstract

Air quality control usually requires a monitoring system of multiple indicators measured at various points in space and time. Hence, the use of space--time multivariate techniques are of fundamental importance in this context, where decisions and actions regarding environmental protection should be supported by studies based on either inter-variables relations and spatial--temporal correlations. This paper describes how canonical correlation analysis can be combined with space--time geostatistical methods for analysing two spatial--temporal correlated aspects, such as air pollution concentrations and meteorological conditions. Hourly averages of three pollutants (nitric oxide, nitrogen dioxide and ozone) and three atmospheric indicators (temperature, humidity and wind speed) taken for two critical months (February and August) at several monitoring stations are considered and space--time variograms for the variables are estimated. Simultaneous relationships between such sample space--time variograms are determined through canonical correlation analysis. The most correlated canonical variates are used for describing synthetically the underlying space--time behaviour of the components of the two sets.

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  • Sandra De Iaco, 2011. "A new space--time multivariate approach for environmental data analysis," Journal of Applied Statistics, Taylor & Francis Journals, vol. 38(11), pages 2471-2483, January.
    • Handle: RePEc:taf:japsta:v:38:y:2011:i:11:p:2471-2483
    DOI: 10.1080/02664763.2011.559206
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    1. De Iaco, S. & Myers, D. E. & Posa, D., 2002. "Space-time variograms and a functional form for total air pollution measurements," Computational Statistics & Data Analysis, Elsevier, vol. 41(2), pages 311-328, December.
    2. Iaco, S. De & Myers, D. E. & Posa, D., 2001. "Space-time analysis using a general product-sum model," Statistics & Probability Letters, Elsevier, vol. 52(1), pages 21-28, March.
    3. De Iaco, S. & Palma, M. & Posa, D., 2005. "Modeling and prediction of multivariate space-time random fields," Computational Statistics & Data Analysis, Elsevier, vol. 48(3), pages 525-547, March.
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